LTAR

A set of tools for forecasting the next step in a multidimensional setting using tensors. In the examples, a forecast is made of sea surface temperatures of a geographic grid (i.e. lat/long). Each observation is a matrix, the entries in the matrix and the sea surface temperature at a particular lattitude/longitude. Cates, J., Hoover, R. C., Caudle, K., Kopp, R., & Ozdemir, C. (2021) "Transform-Based Tensor Auto Regression for Multilinear Time Series Forecasting" in 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 461-466), IEEE doi:10.1109/ICMLA52953.2021.00078.

Last 30 days

This package has been downloaded 120 times in the last 30 days. Enough downloads to make a small wave in the niche community. The curiosity is spreading! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 6 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 1,520 times in the last 365 days. Now we’re talking! This work is officially 'heard of in academic circles', just like those wild research papers on synthetic bananas. The day with the most downloads was Aug 07, 2024 with 22 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.